Demographics

Everyone

Demographics for all included participants.

Demographics
Summary
N Age (years) Education (years) Sex (M/F/O) EHI
621 29.27 (6) 14.31 (2.4) 336/276/9 30.33 (76.91)



Race n
White 452
Black or African American 57
Asian 54
Multiple 51
American Indian or Alaska Native 5
Native Hawaiian or Other Pacific Islander 1
Other 1


Hispanic ethnicity n
No 554
Yes 67


By handedness group

Demographics for included participants, by handedness group (EHI bins).

Handedness N Age (years) Education (years) Sex (M/F/O) EHI
Left 171 29.14 (6.27) 14.25 (2.27) 90/79/2 -80.19 (19.96)
Mixed 78 29.08 (6.08) 14.59 (2.32) 49/27/2 -5.61 (26.7)
Right 372 29.37 (5.88) 14.28 (2.47) 197/170/5 88.68 (15.52)
Left: (EHI <= -40) | Mixed: (-40 < EHI < 40) | Right: (EHI >= 40)


Field x Level x Handedness (binned)

Within each handedness group, do we see the typical field x level interaction? That is, do participants show a relative bias for global shapes in the left visual field (LVF)?

Reaction time

Plots

Error bars show 95% CI.



Statistics

Simple mixed regression model

Reaction time is modeled as a linear effect of field, level, and handedness, using data from every target-present trial with a “go” response:

lmer( rt ~ field*level*handedness + (1 | subject) )


Field by level by handedness interaction (RT)
ANOVA: compare models with vs. without interaction term
npar AIC BIC logLik deviance Chisq Df p.value
9 892,932.348 893,014.217 −446,457.174 892,914.348 - - -
10 892,932.367 893,023.332 −446,456.183 892,912.367 1.982 1 .16


Field by level interaction (RT)
Omnibus F-test
term df sumsq meansq statistic p.value
field 1 585,689.776 585,689.776 8.377 .004
level 1 7,703,209.967 7,703,209.967 110.176 <.0001
handedness 1 1,705,109.75 1,705,109.75 24.388 <.0001
field:level 1 2,486,971.257 2,486,971.257 35.57 <.0001
field:handedness 1 667,410.774 667,410.774 9.546 .002
level:handedness 1 68,165.27 68,165.27 0.975 .32
field:level:handedness 1 45,604.248 45,604.248 0.652 .42
Residuals 65,932 4,609,771,276.462 69,917.055 - -


Field by level by handedness interaction (RT)
Compare effect estimate to zero with emmeans()
field_consec level_consec handedness_consec estimate1 SE df2 asymp.LCL3 asymp.UCL3 z.ratio p.value4
LVF - RVF Local - Global Right - Left 9.786 6.952 Inf −3.84 23.411 1.408 .16
1 A positive number means LVF global bias is stronger in right handers (as predicted by AAH)
2 Z-approximation
3 Confidence level: 95%
4 Two-sided


LVF Global bias by handedness bin (RT)
field_consec level_consec handedness estimate1 SE df2 asymp.LCL3 asymp.UCL3 z.ratio p.value4
LVF - RVF Local - Global Left 17.497 5.747 Inf 6.234 28.76 3.045 .002
LVF - RVF Local - Global Mixed 20.542 8.493 Inf 3.897 37.188 2.419 .02
LVF - RVF Local - Global Right 27.282 3.898 Inf 19.643 34.922 6.999 <.0001
1 A positive number means global bias (faster RT for global)
2 Z-approximation
3 Confidence level: 95%
4 Two-sided, uncorrected


Accuracy

In progress.

Field x Level x Handedness (continuous)

Reaction time

Plots

Statistics

Model RT as a linear effect of field, level, and EHI (continuous):

rt_model_ehi <- lmer( rt ~ field*level*ehi + (1 | subject) ) 

## Use anova() on competing models to test 3-way interaction.
interaction_stats <-
  function(model_with_interaction,
           model_with_no_interaction) {
    return(anova(model_with_interaction, model_with_no_interaction))
  }

rt_model_no_interaction <- update(rt_model_ehi, . ~ . - field:level:ehi)
interaction_anova <- interaction_stats(rt_model_ehi, rt_model_no_interaction)
interaction_anova |>
  as_tibble() |>
  rename(p.value = `Pr(>Chisq)`) |> 
  format_p.value() |> 
  pretty_table() |> 
  tab_header(title = "Field by level by ehi interaction (RT)", 
             subtitle = "ANOVA: compare models with vs. without interaction term")
Field by level by ehi interaction (RT)
ANOVA: compare models with vs. without interaction term
npar AIC BIC logLik deviance Chisq Df p.value
9 1,021,589.879 1,021,672.961 −510,785.94 1,021,571.879 - - -
10 1,021,589.309 1,021,681.622 −510,784.655 1,021,569.309 2.57 1 .11


Estimated global bias by field, for EHI of -100
contrast estimate1 SE df z.ratio p.value
(LVF Local -100) - (LVF Global -100) 31.6 4.199 Inf 7.525 <.0001
(RVF Local -100) - (RVF Global -100) 16.054 4.199 Inf 3.823 .0008
1 Estimated global bias (ms)



Estimated LVF Global Bias for EHI of -100
LVF_global_bias
15.546


Estimated global bias by field, for EHI of +100
contrast estimate1 SE df z.ratio p.value
LVF Local 100 - LVF Global 100 35.347 2.874 Inf 12.297 <.0001
RVF Local 100 - RVF Global 100 7.226 2.885 Inf 2.505 .06
1 Estimated global bias (ms)


Estimated LVF Global Bias for EHI of +100
LVF_global_bias
28.121


\[ 28.121 - 15.546 = 12.575ms \\ 12.575/200 = 0.063ms / EHI unit \] Each unit change in EHI (-100:100) corresponds to a 0.064ms difference in LVF global bias. This is the slope estimate given by the summary function: 

summary(rt_model_ehi)
## Linear mixed model fit by REML ['lmerMod']
## Formula: rt ~ field:level:ehi + field:level + field:ehi + level:ehi +  
##     field + level + ehi + (1 | subject)
##    Data: aah_for_rt_ehi_model
## 
## REML criterion at convergence: 1021574.1
## 
## Scaled residuals: 
##       Min        1Q    Median        3Q       Max 
## -4.840176 -0.597977 -0.165488  0.371242  7.183305 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subject  (Intercept) 27038.4  164.434 
##  Residual             42847.0  206.995 
## Number of obs: 75454, groups:  subject, 621
## 
## Fixed effects:
##                             Estimate  Std. Error   t value
## (Intercept)              693.4635462   7.2813841  95.23788
## fieldRVF                  -2.6540189   2.3114254  -1.14822
## levelGlobal              -33.4735020   2.2930125 -14.59805
## ehi                        0.1482702   0.0881226   1.68254
## fieldRVF:levelGlobal      21.8333224   3.2412040   6.73618
## fieldRVF:ehi              -0.1064488   0.0279636  -3.80669
## levelGlobal:ehi           -0.0187300   0.0277301  -0.67544
## fieldRVF:levelGlobal:ehi   0.0628729   0.0392231   1.60296
## 
## Correlation of Fixed Effects:
##             (Intr) fldRVF lvlGlb ehi    flRVF:G flRVF: lvlGl:
## fieldRVF    -0.160                                           
## levelGlobal -0.161  0.507                                    
## ehi         -0.367  0.059  0.060                             
## fldRVF:lvlG  0.114 -0.713 -0.707 -0.042                      
## fieldRVF:eh  0.059 -0.368 -0.187 -0.159  0.263               
## levelGlbl:h  0.060 -0.187 -0.368 -0.161  0.260   0.507       
## fldRVF:lvG: -0.042  0.263  0.260  0.114 -0.367  -0.713 -0.706

Test for a simple correlation between each subject’s EHI and LVF global bias.

Subject-level correlation: linear model
term estimate std.error statistic p.value1
(Intercept) 18.454 3.104 5.944 <.0001
ehi 0.031 0.038 0.824 .41
1 Two-sided


Subject-level correlation: Spearman's rho
rho statistic p.value1 method alternative
0.039 38,350,957.034 .16 Spearman's rank correlation rho greater
1 One-sided



Accuracy

Plots

Statistics

In progress. Model accuracy as a binomial effect of field, level, and EHI (continuous):

acc_ehi_model <- glmer( rt ~ field*level*ehi + (1 | subject), family = "binomial" )

Test for a simple correlation between each subject’s EHI and LVF global bias.

Subject-level correlation: linear model
term estimate std.error statistic p.value1
(Intercept) 2.484 0.389 6.38 <.0001
ehi 0.001 0.005 0.298 .77
1 Two-sided


Subject-level correlation: Spearman's rho
rho statistic p.value1 method alternative
−0.004 40,060,666.437 .54 Spearman's rank correlation rho greater
1 One-sided